| 1. |
- Ehsan Saffari, Seyed, et al.
(författare)
-
Regression models for analyzing radiological visual grading studies - an empirical comparison
- 2015
-
Ingår i: BMC Medical Imaging. - : BioMed Central. - 1471-2342 .- 1471-2342. ; 15
-
Tidskriftsartikel (refereegranskat)abstract
- Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients. Methods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects. Results: In general, the goodness of fit (AIC and McFadden's Pseudo R-2) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R-2 was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models. Conclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately.
|
|
| 2. |
- Jensen, Kristin, et al.
(författare)
-
Quantitative Measurements Versus Receiver Operating Characteristics and Visual Grading Regression in CT Images Reconstructed with Iterative Reconstruction : A Phantom Study
- 2018
-
Ingår i: Academic Radiology. - : ELSEVIER SCIENCE INC. - 1076-6332 .- 1878-4046. ; 25:4, s. 509-518
-
Tidskriftsartikel (refereegranskat)abstract
- Rationale and Objectives: This study aimed to evaluate the correlation of quantitative measurements with visual grading regression (VGR) and receiver operating characteristics (ROC) analysis in computed tomography (CT) images reconstructed with iterative reconstruction. Materials and Methods: CT scans on a liver phantom were performed on CT scanners from GE, Philips, and Toshiba at three dose levels. Images were reconstructed with filtered back projection (FBP) and hybrid iterative techniques (ASiR, iDose, and AIDR 3D of different strengths). Images were visually assessed by five readers using a four- and five-grade ordinal scale for liver low contrast lesions and for 10 image quality criteria. The results were analyzed with ROC and VGR. Standard deviation, signal-to-noise ratios, and contrast to-noise ratios were measured in the images. Results: All data were compared to FBP. The results of the quantitative measurements were improved for all algorithms. ROC analysis showed improved lesion detection with ASiR and AIDR and decreased lesion detection with iDose. VGR found improved noise properties for all algorithms, increased sharpness with iDose and AIDR, and decreased artifacts from the spine with AIDR, whereas iDose increased the artifacts from the spine. The contrast in the spine decreased with ASiR and iDose. Conclusions: Improved quantitative measurements in images reconstructed with iterative reconstruction compared to FBP are not equivalent to improved diagnostic image accuracy.
|
|
| 3. |
- Persson, Anders, 1953-, et al.
(författare)
-
Volume rendering compared with maximum intensity projection for magnetic resonance angiography measurements of the abdominal aorta
- 2004
-
Ingår i: Acta Radiologica. - : SAGE Publications. - 0284-1851 .- 1600-0455. ; 45:4, s. 453-459
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: To compare the volume rendering technique (VRT) with maximum intensity projection (MIP) for aortic diameter measurements in MR angiography (MRA) data sets.Material and Methods: Existing contrast-enhanced 3-dimensional MRA and digital subtraction angiography (DSA) data sets from 20 patients were analyzed. In each MRA data set, two aortic diameters were measured using MIP and VRT. Agreement with DSA measurements, dependence on rendering parameters, and interobserver agreement were assessed.Results: Diameters measured on MIP with fixed parameters showed no significant difference compared with DSA and with freely selected parameters a slight overestimation relative to DSA. Diameters measured on VRT were larger than on DSA. For both MIP and VRT, the measurements depended on the chosen parameters. The error relative to DSA was larger for VRT than for MIP with fixed parameters but not with freely chosen parameters. Interobserver agreement did not differ significantly.Conclusions: VRT is not suitable for diameter measurements of the abdominal aorta with fixed parameter settings but may be useful with user-selected settings.
|
|
| 4. |
- Poulakis, K, et al.
(författare)
-
Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease
- 2022
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1, s. 4566-
-
Tidskriftsartikel (refereegranskat)abstract
- Understanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.
|
|
| 5. |
- Tomic, Hanna, et al.
(författare)
-
Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
- 2024
-
Ingår i: Medical Imaging 2024 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 1605-7422. - 9781510671546 ; 12925
-
Konferensbidrag (refereegranskat)abstract
- Segmentation of diagnostic radiography images using deep learning is progressively expanding, which sets demands on the accessibility, availability, and accuracy of the software tools used. This study aimed at evaluating the performance of a segmentation model for digital breast tomosynthesis (DBT), with the use of computer-simulated breast anatomy. We have simulated breast anatomy and soft tissue breast lesions, by utilizing a model approach based on the Perlin noise algorithm. The obtained breast phantoms were projected and reconstructed into DBT slices using a publicly available open-source reconstruction method. Each lesion was then segmented using two approaches: 1. the Segment Anything Model (SAM), a publicly available AI-based method for image segmentation and 2. manually by three human observers. The lesion area in each slice was compared to the ground truth area, derived from the binary mask of the lesion model. We found similar performance between SAM and manual segmentation. Both SAM and the observers performed comparably in the central slice (mean absolute relative error compared to the ground truth and standard deviation SAM: 4 ± 3 %, observers: 3 ± 3 %). Similarly, both SAM and the observers overestimated the lesion area in the peripheral reconstructed slices (mean absolute relative error and standard deviation SAM: 277 ± 190 %, observers: 295 ± 182 %). We showed that 3D voxel phantoms can be used for evaluating different segmentation methods. In preliminary comparison, tumor segmentation in simulated DBT images using SAM open-source method showed a similar performance as manual tumor segmentation.
|
|
